Integration of Facial Form and Facial Motion During Face Recognition

人脸识别过程中人脸形态和面部运动的融合

• 批准号: 280741132
• 负责人: Dr. Katharina Dobs
• 金额: --
• 依托单位: CNRS CERCO UMR 5549
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2016-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/280741132?language=en
• 关键词: Integration; Facial; Form; Motion; During

The ability to integrate information provided by the form and motion of faces is a crucial task for social animals like humans. Yet, most of what is known about face perception comes from studies relying on static images of faces. Surprisingly little is known about how and when humans integrate form and motion to recognize a face. This gap is partly due to the previous lack of well-controlled dynamic face stimuli. Moreover, quantitative models linking brain activity to behavior are needed to study the integration of facial form and motion in the human brain. Recently, cue integration research has benefited from the application of so-called optimal cue integration models to investigate multisensory integration. Here, we plan to apply optimal cue integration models to investigate the mechanisms and the neural basis of facial form and motion integration during face recognition.In particular, we aim to address the following research questions: First, what are the specific weights and functions which humans apply when integrating facial motion and form during face recognition? We plan to answer these questions by combining well-controlled dynamic face stimuli, psychophysics and quantitative predictions of optimal cue integration models. Second, which cortical areas are involved in the integration of facial form and motion during face recognition? To this end, in a neuroimaging study, we want to compare behavioral performance to decoded neural activity based on optimal integration theory.The results of this project will have important implications for cognitive, computational and neural models of face perception that currently propose anatomically and functionally distinct neural pathways for the processing of facial form and motion. Moreover, the results will contribute to understanding, diagnosis and therapy of disorders involving dysfunctions of face perception found for example in prosopagnosia or autism spectrum disorders.

对于像人类这样的群居动物来说，整合面部形状和运动提供的信息的能力是一项至关重要的任务。然而，关于脸部感知的大部分已知知识来自于依赖于脸部静态图像的研究。令人惊讶的是，关于人类如何以及何时整合形体和动作来识别一张脸，人们知之甚少。这一差距的部分原因是之前缺乏良好控制的动态面部刺激。此外，需要将大脑活动与行为联系起来的量化模型，以研究面部形状和运动在人脑中的整合。近年来，线索整合的研究得益于最优线索整合模型在多感觉整合研究中的应用。在这里，我们计划应用最优线索整合模型来研究人脸识别过程中面部形状和动作整合的机制和神经基础。特别是，我们的目标是解决以下研究问题：首先，在人脸识别过程中，人类在整合面部运动和形状时采用的具体权重和函数是什么？我们计划通过结合良好控制的动态面部刺激、心理物理学和最佳线索整合模型的定量预测来回答这些问题。第二，在人脸识别过程中，哪些大脑皮层区域参与了人脸形态和运动的整合？为此，在一项神经成像研究中，我们希望基于最优整合理论将行为表现与解码的神经活动进行比较。该项目的结果将对认知、计算和面部感知的神经模型产生重要影响，这些模型目前提出了解剖和功能上不同的神经路径来处理面部形状和运动。此外，研究结果将有助于理解、诊断和治疗涉及面部感知功能障碍的疾病，例如面容失认症或自闭症谱系障碍。